Customer segmentation project using the RFM (Recency, Frequency, Monetary) model to categorize customers based on purchasing behavior. Includes automated data cleaning, RFM scoring, business segment labeling, visualization, and K-Means clustering for unsupervised segmentation.

This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.

This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.